Clustering with Constrained Similarity Learning

• Published in: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03 Vol. 3; pp. 30 - 33
• Main Authors: Okabe, Masayuki, Yamada, Seiji
• Format: Conference Proceeding
• Language: English
• Published: Washington, DC, USA IEEE Computer Society 15.09.2009; IEEE
• Series: ACM Conferences
• Subjects: Clustering algorithms; Computing methodologies > Machine learning > Learning paradigms > Unsupervised learning > Cluster analysis; Conferences; Constraint optimization; Data mining; Data visualization; Information systems > Information retrieval > Evaluation of retrieval results; Information systems > Information retrieval > Retrieval tasks and goals > Clustering and classification; Information systems > Information systems applications > Data mining > Clustering; Intelligent agent; Kernel; Linear matrix inequalities; Mathematics of computing > Mathematical analysis > Mathematical optimization; Optimization methods; Paper technology; Theory of computation > Design and analysis of algorithms > Mathematical optimization
• ISBN: 0769538010; 9780769538013
• DOI: 10.1109/WI-IAT.2009.223

This paper proposes a method of learning a similarity matrix from pairwise constraints for interactive clustering. The similarity matrix can be learned by solving an optimization problem as semi-definite programming where we give additional constraints about neighbors of constrained pairwise data besides original constraints. For interactive clustering, since we can get only a few pairwise constraints from a user, we need to extend such constraints to richer ones. Thus this proposed method to extend the pairwise constraints to space-level ones is effective to interactive clustering. First we formalize clustering with constrained similarity learning, and then introduce the extended constraints as linear constraints. We verify the effectiveness of our proposed method by applying it on a simple clustering task. The results of the experiments shows that our method is promising.